CS计算机代考程序代写 import numpy as np

import numpy as np
from MyDiscriminant import GaussianDiscriminant, GaussianDiscriminant_Diagonal

# load data
df = np.genfromtxt(“training_data.txt”,delimiter=”,”)
dftest = np.genfromtxt(“test_data.txt”,delimiter=”,”)
Xtrain = df[:,0:8]
ytrain = df[:,8]
Xtest = dftest[:,0:8]
ytest = dftest[:,8]

# define the model with a Gaussian Discriminant function (class-dependent covariance)
clf = GaussianDiscriminant(2,8,[0.3,0.7])

# update the model based on training data
clf.fit(Xtrain,ytrain)

# evaluate on test data
predictions = clf.predict(Xtest)
confusion_matrix = np.array([[sum((ytest==1) & (predictions==1)),sum((ytest==2) & (predictions==1))],
[sum((ytest==1) & (predictions==2)),sum((ytest==2) & (predictions==2))]])
print(‘Confusion Matrix for Gaussian Discriminant with class-dependent covariance’)
print(confusion_matrix)

# define the model with a Gaussian Discriminant function (class-independent covariance)
clf = GaussianDiscriminant(2,8,[0.3,0.7],shared_cov=True)

# update the model based on training data
clf.fit(Xtrain,ytrain)

# define the model with a Gaussian Discriminant function (diagonal covariance)
clf = GaussianDiscriminant_Diagonal(2,8,[0.3,0.7])

# update the model based on training data
clf.fit(Xtrain,ytrain)

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